somePairs: Function reporting kernel causality results as a 7-column... In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

Description

This function lets the user choose one of three criteria to determine causal direction by setting typ as 1, 2 or 3. This function reports results for only one criterion at a time unlike the function some0Pairs which summarizes the resulting causal directions for all criteria with suitable weights. If some variables are ‘control’ variables, use someCPairs, C=control.

Usage

 1 somePairs(mtx, dig = 6, verbo = FALSE, typ = 1, rnam = FALSE)

Arguments

 mtx The data matrix in the first column is paired with all others. dig Number of digits for reporting (default dig=6). verbo Make verbo= TRUE for printing detailed steps. typ Must be 1 (default), 2 or 3 for the three criteria. rnam Make rnam= TRUE if cleverly created rownames are desired.

Details

(typ=1) reports ('Y', 'X', 'Cause', 'SD1apd', 'SD2apd', 'SD3apd', 'SD4apd') nameing variables identifying 'cause' and measures of stochastic dominance using absolute values of kernel regression gradients comparing regresson of X on Y with that of Y on X.

(typ=2) reports ('Y', 'X', 'Cause', 'SD1res', 'SD2res', 'SD3res', 'SD4res') and measures of stochastic dominance using absolute values of kernel regression residuals comparing regresson of X on Y with that of Y on X.

(typ=3) reports ('Y', 'X', 'Cause', 'r*X|Y', 'r*Y|X', 'r', 'p-val') containing generalized correlation coefficients r*, 'r' refers to the Pearson correlation coefficient and p-val column has the p-values for testing the significance of Pearson's 'r'.

Value

A matrix containing causal identification results for one criterion. The first column of the input mtx having p columns is paired with (p-1) other columns The output matrix headings are self-explanatory and distinct for each criterion Cr1 to Cr3.

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

References

H. D. Vinod 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, doi: 10.1080/03610918.2015.1122048